The aim of image-guided therapy is to target, access, and remove lesions without damaging the normal and functioning brain tissue, thus preserving essential neurological function. Therefore, it is necessary to understand and visualize the structural and functional anatomy through sophisticated statistical analytic, monitoring and validation methodology. It is also important to distinguish infiltrating tumors from the surrounding normal tissue (based mainly on the visual appearance of the lesion). This is an especially challenging task, particularly because it is difficult to clearly recognize and define tumor margins. Even in the most ideal circumstances, this decision is difficult to obtain solely intra-operatively. The short-term goal is to support all to work closely together on utilizing the appropriate statistical methods to support and analyze preoperative surgical planning and intraoperative MRI-guidance from all other core projects. Postoperative outcome analysis will be conducted using multivariate methods. The long-term goal is to develop statistical analytic and validation methods for (1) better localization of lesions, (2) accurate definition of tumor margins, and (3) optimization of surgical strategies. The relevance of this project to public health is as follows: The biostatistical methodology will generate major advances for both and applications.The outcomes analytic models mayultimately be embedded into the clinical workflow related to image- guided therapy. If the application is funded, this description, as is, will become public information. Therefore, do not include proprietary/confidential information.